arXiv:2607. 02338v1 Announce Type: cross Abstract: Hierarchical Navigable Small World (HNSW) graphs serve as the industry standard due to their logarithmic complexity and strong empirical performance.
By Minghao Li, Raghav Mittal, Sanjivni Rana, Suraj Shetiya, Gautam Das, Nick Koudas
arXiv:2601. 16509v2 Announce Type: replace-cross Abstract: The $k$-nearest neighbors ($k$NN) algorithm is a cornerstone of non-parametric classification in artificial intelligence, yet its deployment in large-scale applications is persistently constrained by the computational trade-off between inference speed and accuracy.
By Jiaye Li, Hang Xu, Shichao Zhang
The paper investigates methods for estimating average distances in large static graphs, comparing a graph sampling approach (Random Walk) with landmark-based techniques such as the Size Estimation Framework (SEF) and the Eppstein‑Wang (EW) algorithm. Random Walk proved unreliable for small samples and costly for larger ones, while landmark methods using HyperLogLog were more efficient. Experiments on undirected, unweighted graphs showed that the EW algorithm achieves very low error (≈0.02%) and can use a subset of only 100 nodes for accurate estimation, outperforming SEF in accuracy and speed.
By Kartikey Ahlawat
arXiv:2606. 18520v1 Announce Type: cross Abstract: Computing geometric representations of data is a cornerstone of modern machine learning, typically achieved by training dual encoders which map queries and documents into a shared embedding space.
By Prashant Gokhale, Piotr Indyk, Yuhao Liu, Sandeep Silwal, Tony Chang Wang, Haike Xu
arXiv:2503. 06396v2 Announce Type: replace Abstract: The minimum vertex cover (MVC) problem seeks to identify the smallest set of vertices that cover all edges in an undirected graph.
By Chanjuan Liu, Qiqi Bao, Yu Zhang, Enqiang Zhu
arXiv:2609.36359v1 Announce Type: new
Abstract: Graph-based approximate nearest neighbor search (ANNS) is widely used for large-scale semantic search. Its indices are constructed primarily based on g...
By Fangzhou Wu, Haike Xu, Sandeep Silwal